Evolutionary Computation Introduction

1. Evolutionary ComputationIntroduction Peter Andras peter.andras@ncl.ac.uk www.staff.ncl.ac.uk/peter.andras/lectures

2. Overview • Biological inspiration • Artificial genes • Learning by evolution • Artificial evolution • Learning by artificial evolution

3. Biological inspiration • Evolution: • Darwin • from bacteria to sponges, insects, fishes, and mammals • from simple organs to complex ones • from randomly spread neurons to highly organized large brains

4. Biological inspiration • Foundations: • nucleic acids: adenin, citozin, guanin, timin, uracil • DNA • chromosomes • genes • RNA, proteins, cells

5. Biological inspiration • Adaptation by evolution: • ecological niche: a set of ecological conditions (e.g., food resources, predators, other environmental risks, threats and opportunities); • conquering new ecological niches (e.g., islands) • development of new species that are able to use the opportunities provided by a new niche and avoid the related dangers;

6. Biological inspiration • Adaptation: • development of new behaviours and organs; • new cells and cell behaviours; • new proteins; • new genes;

7. Artificial genes • Idea: • copying natural evolution by emulating genes and their evolution; • Objective: • developing adaptive solutions of some problems;

8. Artificial genes • Artificial world: • world of problems; • Artificial individuals: • solutions of the problems • genes encode features of the problem solutions

9. Artificial genes • Discrete feature encoding: • e.g., 0 and 1 for the presence or absence of the features; • chromosomes: 001110101110; • the genes do not represent necessarily full features;

10. Artificial genes • Continuous type feature encoding: • e.g., features encoded by real numbers; • chromosomes: multi-dimensional real vectors; • usually genes directly encode features;

11. Learning by evolution • Learning: • learning = adaptation • adaptation = optimisation • optimisation criteria: fitness in the given environmental conditions;

12. Learning by evolution • Exchanging and combining genes: • sexual crossover + 

13. Learning by evolution • Mutation: • random changes of the genes 

14. Learning by evolution • Inheritance: • the offspring inherits the properties of their parents; • some combinations are lethal; • the inherited properties range from similar proteins to similar behaviours;

15. Learning by evolution • New species: • slow evolution; • accumulating minor changes; • modifications of organ functionality; • selection of some variants of standard features (e.g., feather colours); • emergence of new behaviours, organs;

16. Learning by evolution • Mating success: • features that better fit the environmental niche increase the chance of the individual to get mates and reproduce; • individuals with higher fitness have more offspring; • the genes of the successful individuals spread within the population and become dominant; • genes that cause evolutionary advantage in mutated individuals become general;

17. Learning by evolution • Evolutionary optimisation: • increased fitness in the ecological niche; • mutation is responsible for new genes (proteins, cells, organs, behaviours); • crossover is responsible for passing over the new genes; • fitness based mating success is responsible for the emergence of domination of genes that increase fitness;

18. Artificial evolution • Evolution of a population of problem solutions: • individuals are the problem solution; • each solution is characterized by its features encoded by the genes; • evolution by genetic operators and offspring generation;

19. Artificial evolution • Mutation operator: • randomly change the genes encoding the solution features; • e.g., changing a 0 into a 1 and inversely; • e.g., minor modification of a feature encoded by a real number;

20. Artificial evolution • Crossover operator: • defines how to select exchanged parts of the genetic material; • e.g., randomly selecting a chromosome splitting position;

21. Artificial evolution • Directed operators: • preferential selection of some genes for mutation or some segments of the chromosome for crossover; • the preferential selection is based on monitoring, which components of the solution contribute to bad or good performance;

22. Artificial evolution • Constrained operators: • mutation constraints: some simultaneous mutations are not allowed, others are enforced; • crossover constraints: some chromosome segments are allowed to be exchanged only for some chromosome segments with specified location;

23. Artificial evolution • Optimisation energy function: • fitness measure = problem solving performance • problem solving performance of the individuals are evaluated with a random sample of the potential problems;

24. Artificial evolution • Mating potential: • it is based on the problem solving performance; • the number of the offspring of the individuals depends on their mating potential; • high fitness individuals have many offspring that inherit at partly their features;

25. Artificial evolution • Many parent mating: • the crossover applies to the mix of all parents;

26. Learning by artificial evolution • Problem solving performance optimisation: • the average performance of the population increases; • the best performing individuals represent very good solutions after long enough evolution;

27. Learning by artificial evolution • Key features: • proper feature coding; • proper evolutionary operators; • proper fitness evaluation; • proper mating selection;

28. Learning by artificial evolution • Feature coding: • the important solution features should be encoded; • if it is not clear what is important and what is not, better to encode more features than less features; • the feature coding and the decoding of the code should not be ambiguous;

29. Learning by artificial evolution • Evolutionary operators: • the result of applying evolutionary operators should be meaningful; • the crossover should result individuals that inherit their parents properties;

30. Learning by artificial evolution • Fitness evaluation: • the fitness function should be closely related to the effective problem solving performance;

31. Learning by artificial evolution • Mating potential determination: • the more fit individuals should have more offspring; • the drastic elimination of less fit individuals may lead to the elimination of genes that are sleeping but may become important for the achievement of very high performance;

32. Learning by artificial evolution • Problems: • too narrow spread of performances: it is likely that there is little genetic variation in the population; • too large spread of the performances: it is possible that the encoding of features or the genetic operators are not functioning properly; • too slow increase of the average performance: it is possible that the encoding of features or the genetic operators are not functioning properly;

33. Summary • evolution leads to niche adapted new species; • the basis of evolution are the genes; • new genes may lead to new proteins, cells, organs, behaviours, which may increase the fitness of the biological organism; • evolutionary adaptations spread by mating and by higher mating success of those who are more fit to the ecological niche; • evolutionary learning means optimisation of the fitness;

34. Summary • artificial genes encode features of solutions of some problems, the encoding can be discrete or continuous; • artificial evolution works by genetic operators; • genetic operators: mutation, crossover, directed operators, constrained operators; • mating potential depends on problem solving performance; • having appropriate feature encoding, evolutionary operators, fitness function and mating potential determination, the artificial evolution leads to high performance solutions of the problem;